{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting pressure bump diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and get Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pencilnew as pcn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import ceil\n",
    "\n",
    "def takespread(sequence, num):\n",
    "    length = float(len(sequence))\n",
    "    for i in range(num):\n",
    "        yield sequence[int(ceil(i * length / num))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sim = pcn.get_sim(); sim = sim.update()\n",
    "#grid = pcn.read.grid()\n",
    "grid = sim.grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amplitude: 0.01\n"
     ]
    }
   ],
   "source": [
    "Lx = sim.grid.Lx\n",
    "x = sim.grid.x\n",
    "\n",
    "pb_type = sim.param['pb_type']\n",
    "#pb_amplitude = sim.param['pb_amplitude']\n",
    "pb_amplitude = 0.01\n",
    "\n",
    "print('Amplitude: '+str(pb_amplitude))\n",
    "\n",
    "if pb_type == 'sinwave-x':\n",
    "    pb_profile = pb_amplitude*np.sin(2*np.pi/Lx*x)\n",
    "    \n",
    "else:\n",
    "    print('! ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing old pressure gradient and new gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "old global pressure gradient: -0.100000001\n",
      "new pressure gradient profile AMPLITUDE: 0.00999999978\n",
      "!! The amplitude is multiplied with the global gradient when applied!! see below!\n"
     ]
    }
   ],
   "source": [
    "print('old global pressure gradient: '+str(sim.param['beta_glnrho_global'][0]))\n",
    "print('new pressure gradient profile AMPLITUDE: '+str(sim.param['pb_amplitude']))\n",
    "print('!! The amplitude is multiplied with the global gradient when applied!! see below!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa0eb093160>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAD8CAYAAABkbJM/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npZyiAUMppZRT/h9JOAU75QDSQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0eb039940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "old_pgrad = sim.param['beta_glnrho_global'][0]*x\n",
    "new_pgrad = old_pgrad + sim.param['beta_glnrho_global'][0]*pb_profile\n",
    "\n",
    "ax.plot(x, old_pgrad, color='blue', label='old pressure gradient')\n",
    "ax.plot(x, new_pgrad, color='red', label='new pressure gradient')\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots of a 2D (x-y) simulation\n",
    "With new pressure bump as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "min=0.0 - max=0.0\n",
      "0.00688334\n",
      "min=0.00686864 - max=0.00689788\n",
      "0.0135364\n",
      "min=0.0135246 - max=0.0135485\n",
      "0.0200981\n",
      "min=0.0200953 - max=0.0201006\n",
      "0.0265403\n",
      "min=0.0265249 - max=0.0265553\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEJCAYAAACUk1DVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkCQBFoIkqbEQJEmAhSBJav4/LBNh8a3uaOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0eab55e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 5\n",
    "fig, axes = plt.subplots(1,N,sharey=True,sharex=True)\n",
    "varlist = sim.get_varlist()\n",
    "varlist = takespread(varlist, N)\n",
    "\n",
    "for var, ax in zip(varlist, axes):\n",
    "    VAR = pcn.read.var(var_file=var, trim_all=True, quiet=True)\n",
    "    a = ax.imshow(VAR.ux[0], \n",
    "                  interpolation='none', \n",
    "                  extent=[-Lx, Lx, -Lx, Lx], \n",
    "                  #vmin = 0.026,\n",
    "                  #vmax = 0.027,\n",
    "                  aspect=1)\n",
    "    print(np.mean(VAR.ux))\n",
    "    ax.set_title(str(VAR.t/(np.pi*2))[:4]+' $T_\\mathrm{orb}$');\n",
    "    print('min='+str(VAR.ux.min())+' - max='+str(VAR.ux.max()))\n",
    "\n",
    "    \n",
    "#fig.colorbar(a)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " |XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX| 100.0% \n"
     ]
    }
   ],
   "source": [
    "varlist = sim.get_varlist()\n",
    "\n",
    "Nt = np.size(varlist)\n",
    "Nx = np.size(sim.grid.x)\n",
    "\n",
    "ARRAY = np.zeros((Nt, Nx))\n",
    "\n",
    "for i, var in zip(range(Nt), varlist):\n",
    "    pcn.backpack.printProgressBar(i, Nt-1)\n",
    "    VAR = pcn.read.var(var_file=var, trim_all=True, quiet=True)\n",
    "    ARRAY[i] = VAR.rho[0].mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa0eaceec50>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAABPCAYAAAA3KrvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0eacda5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(ARRAY.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa0eb088550>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEDCAYAAAAx0WHLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKtxztXCkjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0eb6d3b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO4AAAD8CAYAAABw8JiyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKhsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0ea8d7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAAD8CAYAAAC8aaJZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxqE3axmH3qxlHHqzlnHozVrGoTdrmf8HOQbRnSJZpecAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0eaff95c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "av = pcn.read.aver('y')\n",
    "plt.figure()\n",
    "a = plt.imshow(av.y.rhomxz[:,:,0], extent=[-Lx, Lx, 0, av.t[-1]])\n",
    "plt.colorbar()\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(av.y.uxmxz[:,:,0], extent=[-Lx, Lx, 0, av.t[-1]])\n",
    "plt.colorbar()\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(av.y.uymxz[:,:,0], extent=[-Lx, Lx, 0, av.t[-1]])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "-0.00274544\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAADICAYAAADvPoogAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0eaa92b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2)\n",
    "varlist = sim.get_varlist()\n",
    "varlist = [varlist[0],varlist[-1]]\n",
    "\n",
    "for var, ax in zip(varlist, axes):\n",
    "    VAR = pcn.read.var(var_file=var, trim_all=True, quiet=True)\n",
    "    ax.imshow(VAR.uy[0])\n",
    "    print(np.mean(VAR.uy))\n",
    "    ax.set_title(str(VAR.t/(np.pi*2))+'$T_\\mathrm{orb}$')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
